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Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Multi-threading and one-sided communication in parallel LU factorization
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ACM Transactions on Mathematical Software (TOMS)
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PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
hwloc: A Generic Framework for Managing Hardware Affinities in HPC Applications
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
StarPU: a unified platform for task scheduling on heterogeneous multicore architectures
Concurrency and Computation: Practice & Experience - Euro-Par 2009
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From serial loops to parallel execution on distributed systems
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SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Feature-based analysis of large-scale spatio-temporal sensor data on hybrid architectures
International Journal of High Performance Computing Applications
International Journal of High Performance Computing Applications
Multifrontal QR factorization for multicore architectures over runtime systems
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
Computers & Mathematics with Applications
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The frenetic development of the current architectures places a strain on the current state-of-the-art programming environments. Harnessing the full potential of such architectures is a tremendous task for the whole scientific computing community. We present DAGuE a generic framework for architecture aware scheduling and management of micro-tasks on distributed many-core heterogeneous architectures. Applications we consider can be expressed as a Direct Acyclic Graph of tasks with labeled edges designating data dependencies. DAGs are represented in a compact, problem-size independent format that can be queried on-demand to discover data dependencies, in a totally distributed fashion. DAGuE assigns computation threads to the cores, overlaps communications and computations and uses a dynamic, fully-distributed scheduler based on cache awareness, data-locality and task priority. We demonstrate the efficiency of our approach, using several micro-benchmarks to analyze the performance of different components of the framework, and a linear algebra factorization as a use case.